Large buildings often incorporate computerized control systems that manage the operation of different subsystems, such as heating, ventilation and air conditioning (“HVAC”). In addition to ensuring that the subsystem performs as desired, the control system typically operates the associated equipment as efficiently as possible.
A large entity may have numerous buildings under common management, such as on a university campus or a chain of stores located in different cities. To accomplish this, the controllers in each building gather data regarding performance of the building subsystems so that the data can be analyzed at the central monitoring location.
With the cost of energy increasing, building owners are looking for ways to manage and conserve utility consumption. In addition, the cost of electricity for large consumers may be based on the peak use during a billing period. Thus, high consumption of electricity during a single day can affect the rate at which the service is billed during an entire month. Moreover, certain preferential rate plans require a customer to reduce consumption upon the request of the utility company, such as on days of large service demand throughout the entire utility distribution system. Failure to comply with the request usually results in stiff monetary penalties which raises the energy cost significantly above that for an unrestricted rate plan. Therefore, energy usage should be precisely measured to determine the best rate plan and implement processes to ensure that operation of the facility does not inappropriately cause an increase in utility costs.
The ability to analyze energy or utility consumption is also of importance in identifying abnormal consumption. Abnormal energy or utility consumption may indicate malfunctioning equipment or other problems in the building. Therefore, precisely monitoring utility usage and detecting abnormal consumption levels can indicate when maintenance or replacement of the machinery is required.
As a consequence, sensors are being incorporated into building management systems to measure utility usage for the entire building, as well as specific subsystems such as heating, ventilation and air conditioning equipment. These management systems collect and store massive quantities of utility use data that can be overwhelming to the facility operator when attempting to analyze that data in an effort to detect anomalies.
Sampled data is only as good as the sensor that measures and records the data. There are many sources of noise in sampled data and one of the easiest to control is that caused by quantization. Simply put, a sampling sensor rounds its reading to the nearest level of its resolution. Quantization error or level is a measure of the amount of round off that occurs. Such quantization error typically occurs in analog sampling and relates to measurement of sensor resolution/precision. By properly configuration of the sensor, the quantization error can be reduced to a reasonable level.
It is generally known how to manually analyze quantization error. However, such conventional methods of estimating quantization error have several disadvantages including tedious inspection of large amounts of energy usage data.
Accordingly, it would be advantageous to provide a system and method for estimating the precision of sampled data. It would also be advantageous to provide a system and method for estimating quantization noise in an energy usage sensor. It would be desirable to provide for a system and method for estimating quantization noise in sampled data having one or more of these or other advantageous features.
This invention describes an algorithm that can examine a series of sampled data points and from them provide a good estimate of the amount of quantization error that is occurring in both absolute quantities and as a percentage of the values being sampled. The advantages to this are that the level of this error can be determined automatically and can be used to quickly flag sensors that should be corrected. It has a clear application in our facility monitoring applications.